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Optimal Linear Combination of Facial Regions for Improving Identification Performance
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Representation Plurality and Fusion for 3-D Face Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Progressive Coding of 3-D Objects Based on Overcomplete Decompositions
IEEE Transactions on Circuits and Systems for Video Technology
Robust sparse bounding sphere for 3D face recognition
Image and Vision Computing
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This paper addresses the problem of 3D face recognition using simultaneous sparse approximations on the sphere. The 3D face point clouds are first aligned with a fully automated registration process. They are then represented as signals on the 2-sphere in order to preserve depth and geometry information. Next, we implement a dimensionality reduction process with simultaneous sparse approximations and subspace projection. It permits to represent each 3D face by only a few spherical functions that are able to capture the salient facial characteristics, and hence to preserve the discriminant facial information. We eventually perform recognition by effective matching in the reduced space, where linear discriminant analysis can be further activated for improved recognition performance. The 3D face recognition algorithm is evaluated on the FRGC v.1.0 data set, where it is shown to outperform classical state-of-the-art solutions that work with depth images.